This paper reports an algorithm to automatically identify the chain of events leading to a disruption, evaluating the so-called reference warning time. This time separates the plasma current flat-top of each disrupted discharge into two parts: a non-disrupted part and a pre-disrupted one. The algorithm can be framed into the anomaly detection techniques as it aims to detect the off-normal behavior of the plasma. It is based on a statistical analysis of a set of dimensionless plasma parameters computed for a selection of discharges from the JET experimental campaigns. In every data-driven model, such as the generative topographic mapping (GTM) predictor proposed in this paper, it is indeed necessary to label the samples needed for training the model itself. The samples describing the disruption-free behavior are extracted from the plasma current flat-top phase of the regularly terminated discharges. The disrupted space is described by all the samples belonging to the pre-disruptive phase of each disruptive discharge in the training set. Note that a proper selection of the pre-disruptive phase plays a key role in the prediction performance of the model. Moreover, these models, which are highly dependent on the training input space, may be particularly prone to degradation as the operational space of any experimental machine is continuously evolving. Hence, a regular schedule of model review and retrain must be planned. The proposed algorithm avoids the cumbersome and time-consuming manual identification of the warning times, helping to implement a continuous learning system that could be automated, despite being offline. In this paper, the automatically evaluated warning times are compared with those obtained with a manual analysis in terms of the impact on the mapping of the JET input parameter space using the GTM methodology. Moreover, the algorithm has been used to build the GTM of recent experimental campaigns, with promising results.

本文报告了一种算法，该算法可自动识别导致中断的事件链，并评估所谓的参考警告时间。这次将每个中断放电的等离子体电流平顶分为两部分：未中断的部分和预中断的部分。该算法可用于异常检测技术，因为它旨在检测等离子体的异常行为。它基于对一组无量纲血浆参数的统计分析，这些参数计算用于从JET实验活动中选择放电。在每个数据驱动的模型中，例如本文提出的生成地形图（GTM）预测器，确实有必要标记训练模型本身所需的样本。描述无干扰行为的样本是从规则终止的放电的等离子电流平顶阶段提取的。破坏空间由属于训练集中每个破坏性排放的破坏前阶段的所有样本描述。请注意，正确选择中断前阶段在模型的预测性能中起关键作用。而且，这些模型高度依赖于训练输入空间，因为任何实验机器的操作空间都在不断发展，所以它们特别容易退化。因此，必须计划定期进行模型审查和再培训的时间表。所提出的算法避免了繁琐且耗时的手动识别警告时间，从而有助于实现可自动执行的连续学习系统，尽管处于离线状态。在本文中，将自动评估的警告时间与使用GTM方法对JET输入参数空间的映射产生影响的手动分析获得的警告时间进行比较。此外，该算法已用于构建近期实验活动的GTM，并取得了可喜的结果。